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Toward a Global Tuamotu Archipelago Coconut Trees Sensing Using High Resolution Optical Data

Abstract : This study is part of a regeneration program of the coconut grove of French Polynesia where most coconut palm trees of the Tuamotu archipelago were planted in the 1980's following the various hurricanes that had struck islands. The French Polynesia government acquired one-meter pansharpened RGB Ikonos images over the Tuamotu archipelago. To exploit these data, a pilot study is conducted on the island of Tikehau, well-known from the specialists and easily accessible from Tahiti. A Maximum Likelihood (ML) classification is performed to segment the high vegetation in images. Thus, a Support Vector Machines (SVM) classification allows the high vegetation to be classified in different patterns. And finally, a robust segmentation process based on markers controlled watershed segmentation is proposed to extract tree crows. Through the ground mission, the trees detection accuracy is estimated which is then used to compute the number of trees the closest to the reality by applying a weighted factor to the number of trees located in each class.
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Submitted on : Wednesday, January 8, 2014 - 12:17:51 PM
Last modification on : Friday, January 21, 2022 - 3:21:40 AM
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Raimana Teina, Dominique Béréziat, Benoît Stoll, Sébastien Chabrier. Toward a Global Tuamotu Archipelago Coconut Trees Sensing Using High Resolution Optical Data. IGARSS - IEEE International Geoscience and Remote Sensing Symposium, Jul 2008, Boston, United States. pp.797-800, ⟨10.1109/IGARSS.2008.4779114⟩. ⟨inria-00582826⟩



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